###Total Time taken
---
title: "Barcorde Based Student Record System"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "twitter", "facebook", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(ggplot2)
library(rpivotTable)
library(plotly)
library(dplyr)
library(highcharter)
library(openintro)
library(scales)
```
```{r}
roi<-read.csv("roi.csv")
nps<-read.csv("nps.csv")
survey<-read.csv("surveys.csv")
Si<-read.csv("system_incidents.csv")
nf<-read.csv("new_features.csv")
ip<-read.csv("investment_proposals.csv")
et<-read.csv("employee_training.csv")
av<-read.csv("Average_response_time.csv")
```
```{r}
mycolors<-c("grey","blue","maroon","#D4AF37")
```
Internal Business Processes Perspective
===========================================
Row
---------------------------------------
### Total Number of Trainings
```{r}
valueBox(sum(et$Employee.Training),
icon = "fa-bicycle")
```
### Total Number of New features
```{r}
valueBox(sum(nf$New.Features),
icon = "fa-bicycle")
```
### Trainings Per New Feature
```{r}
Traingings_per_new_feature=sum(et$Employee.Training)/sum(nf$New.Features)
gauge(round(Traingings_per_new_feature,
digits = 2),
min = 0,
max = 3,
gaugeSectors(success = c(2, 3),
warning = c(0.5,1 ),
danger = c(0, 0.5),
colors = c("green", "yellow", "red")))
```
Row
---------------------------------------
```{r}
p2 <- et %>%
group_by(et$Year.2020) %>%
summarise(et$Employee.Training) %>%
plot_ly(labels = ~et$Year.2020,
values = ~et$Employee.Training,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F))
p2
```
### Employees' Trainings
```{r}
p1 <- nf %>%
group_by(nf$Year) %>%
summarise(sum(nf$New.Features)) %>%
plot_ly(x = ~nf$Year,
y = ~nf$New.Features,
type = 'bar',
marker = list(color = 'blue')) %>%
layout(xaxis = list(title = "Year"),
yaxis = list(title = 'Number of Trainings'))
p1
```
###
```{r}
p1 <- et %>%
group_by(et$Year.2020) %>%
summarise(sum(et$Employee.Training)) %>%
plot_ly(x = ~et$Employee.Training,
y = ~et$Year.2020,
type = 'bar',
marker = list(color = 'blue')) %>%
layout(xaxis = list(title = "Year"),
yaxis = list(title = 'Number of Trainings'))
p1
```
Innovation and Learning Perspective
===========================================
Row
--------------------------------------
### Total time taken
```{r}
valueBox(sum(av$Time.taken.to.respond..sec.),
icon = "fa-bicycle")
```
### Average Response Time
```{r}
gauge(round(5.5,
digits = 2),
min = 0,
max = 10,
gaugeSectors(success = c(7, 10),
warning = c(5.,8 ),
danger = c(0, 4),
colors = c("green", "yellow", "red")))
```
### Mode of the Response Time
```{r}
gauge(round(12,
digits = 2),
min = 0,
max = 10,
gaugeSectors(success = c(12, 15),
warning = c(8,11 ),
danger = c(0, 7),
colors = c("green", "yellow", "red")))
```
###Total Time taken
```{r}
```
Row
--------------------------------------
### System Incidents
```{r}
p3 <- Si %>%
group_by(Si$Year.2022) %>%
summarise(sum(Si$System.uptime.incidents)) %>%
plot_ly(x = ~Si$Year.2022,
y = ~Si$System.uptime.incidents,
type = 'bar',
marker = list(color = '#D4AF37')) %>%
layout(xaxis = list(title = "2022"),
yaxis = list(title = 'Number of system uptime incidents'))
p3
```
Customer's Perspective
===========================================
Row
------------------------------------------------------
### Average NPS
```{r}
gauge(round(mean(nps$Net.Promoter),
digits = 2),
min = -100,
max = 100,
gaugeSectors(success = c(50, 100),
warning = c(0,50 ),
danger = c(-100, 0),
colors = c("green", "yellow", "red")))
```
### 2019 NPS
```{r}
gauge(round(85,
digits = 2),
min = -100,
max = 100,
gaugeSectors(success = c(60, 100),
warning = c(0,60 ),
danger = c(-100, 0),
colors = c("green", "yellow", "red")))
```
### 2020 NPS
```{r}
gauge(round(60,
digits = 2),
min = -100,
max = 100,
gaugeSectors(success = c(70, 100),
warning = c(0,70 ),
danger = c(-100, 0),
colors = c("green", "yellow", "red")))
```
### 2021 NPS
```{r}
gauge(round(75,
digits = 2),
min = -100,
max = 100,
gaugeSectors(success = c(60, 100),
warning = c(0,60 ),
danger = c(-100, 0),
colors = c("green", "yellow", "red")))
```
### 2022 NPS
```{r}
gauge(round(89,
digits = 2),
min = -100,
max = 100,
gaugeSectors(success = c(60, 100),
warning = c(0,60 ),
danger = c(-100, 0),
colors = c("green", "yellow", "red")))
```
Row
--------------------------------------------------
### NPS by Year
```{r}
p3 <- nps %>%
group_by(nps$Year) %>%
summarise(sum(nps$Net.Promoter)) %>%
plot_ly(x = ~nps$Year,
y = ~nps$Net.Promoter,
type = 'bar',
marker = list(color = '#D4AF37')) %>%
layout(xaxis = list(title = "2022"),
yaxis = list(title = 'Net promoter Score'))
p3
```
### Number of Surveys
```{r}
p3 <- survey %>%
group_by(survey$Year) %>%
summarise(sum(survey$Student.Surveys)) %>%
plot_ly(x = ~survey$Year,
y = ~survey$Student.Surveys,
type = 'bar',
marker = list(color = 'blue')) %>%
layout(xaxis = list(title = "2022"),
yaxis = list(title = 'Number of Survey '))
p3
```
Financial Perspective
===========================================
Row
----------------------------------------------
### Total Investment Aount
```{r}
MillionForm <- dollar_format(prefix = "$",suffix="M")
valueBox(MillionForm((sum(ip$Investment.Amount..million.))),
icon = "fa-money",
color='white')
```
### Total Approved Investment Amount
```{r}
MillionForm <- dollar_format(prefix = "$",suffix="M")
valueBox(MillionForm(25),
icon = "fa-money",
color='white')
```
### Average Expected Roi
```{r}
gauge((scales::percent(mean(ip$Expected.ROI....)/100)),
min = 0,
max = 50,
gaugeSectors(success = c(25, 50),
warning = c(24, 15),
danger = c(0, 14),
colors = c("green", "darkorange", "red")))
```
Row
-----------------------------------------------
### Total Invesment Amount by Proposal Type
```{r}
p4 <- ip %>%
filter(!is.na(ip$Proposal.Name)) %>%
group_by(ip$Proposal.Name) %>%
plot_ly(labels = ~ip$Proposal.Name,
values = ~ip$Investment.Amount..million.,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F))
p4
```
### Total Invesment Amount by Year
```{r}
p3 <- ip %>%
group_by(ip$Year) %>%
summarise(sum(ip$Investment.Amount..million.)) %>%
plot_ly(x = ~ip$Year,
y = ~ip$Investment.Amount..million.,
type = 'bar',
marker = list(color = 'blue')) %>%
layout(xaxis = list(title = "Year"),
yaxis = list(title = 'Investment Amount '))
p3
```
### Investment Amount by Status
```{r}
p4 <- ip %>%
filter(!is.na(ip$Proposed.Status)) %>%
group_by(ip$Proposed.Status) %>%
plot_ly(labels = ~ip$Proposed.Status,
values = ~ip$Investment.Amount..million.,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F))
p4
```
Row
-----------------------------------------------
### Expected ROI by Proposal Type
```{r}
p4 <- ip %>%
filter(!is.na(ip$Proposal.Name)) %>%
group_by(ip$Proposal.Name) %>%
plot_ly(labels = ~ip$Proposal.Name,
values = ~ip$Expected.ROI....,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F))
p4
```
### Expected ROI by Year
```{r}
p3 <- ip %>%
group_by(ip$Year) %>%
summarise(sum(ip$Expected.ROI....)) %>%
plot_ly(x = ~ip$Year,
y = ~ip$Expected.ROI....,
type = 'bar',
marker = list(color = 'blue')) %>%
layout(xaxis = list(title = "Year"),
yaxis = list(title = 'Expected ROI in % '))
p3
```
### Expected ROI by Status
```{r}
p4 <- ip %>%
filter(!is.na(ip$Proposed.Status)) %>%
group_by(ip$Proposed.Status) %>%
plot_ly(labels = ~ip$Proposed.Status,
values = ~ip$Expected.ROI....,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F))
p4
```
# International business occurs in many different formats: The movement of goods from country to another (exporting, importing, trade) Contractual agreements that allow foreign firms to use products, services, and processes from other nations (licensing, franchising)